## Error in get(paste0(generic, ".", class), envir = get_method_env()) :
## object 'type_sum.accel' not found
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
# Set global chunk options
knitr::opts_chunk$set(fig.height = 8, fig.width = 8)
A - Continental US A11
state_frequencies <- CensusHLA::census_adjusted_nmdp_hla_frequencies_by_state |> dplyr::filter(allele == 'A*11:01')
out_data <- state_frequencies |>
dplyr::ungroup() |>
dplyr::group_by(region, census_region, fips) |>
dplyr::summarize(gf = sum(us_2020_nmdp_gf))
## `summarise()` has grouped output by 'region', 'census_region'. You can override
## using the `.groups` argument.
gg_state <- usmap::plot_usmap(
data = out_data,
regions = "states",
#exclude = c('Alaksa','Hawaii'),
exclude = c('AK', 'HI'),
values = "gf",
color = "black",
linewidth = 0.1
) +
viridis::scale_fill_viridis(option = "plasma", direction = 1)
gg_state
B - A11 by County
info_by_county <- CensusHLA::census_adjusted_nmdp_hla_frequencies_by_county |>
dplyr::filter(!(state %in% c('Alaska','Hawaii'))) |>
dplyr::filter(allele == 'A*11:01')
out_data <- info_by_county |>
dplyr::ungroup() |>
dplyr::filter(allele == 'A*11:01') |>
dplyr::group_by(region, state, census_region, county, fips, loci, allele) |>
dplyr::summarize(gf = sum(us_2020_nmdp_gf)) |>
dplyr::filter(!(is.na(gf))) |>
# Create a STATEFP and COUNTYFP column by breaking the fips column on the 3rd character to the end
dplyr::mutate(STATEFP = substr(fips, 1, 2),
COUNTYFP = substr(fips, 3, nchar(fips)))
## `summarise()` has grouped output by 'region', 'state', 'census_region',
## 'county', 'fips', 'loci'. You can override using the `.groups` argument.
out_data <-
out_data |>
dplyr::mutate(
county = dplyr::case_when(
state == "Connecticut" &
census_region == "Litchfield County, Connecticut" ~ "Northwest Hills Planning Region",
state == "Connecticut" &
census_region == "Hartford County, Connecticut" ~ "Capitol Planning Region",
state == "Connecticut" &
census_region == "Middlesex County, Connecticut" ~ "Lower Connecticut River Valley Planning Region",
state == "Connecticut" &
census_region == "Windham County, Connecticut" ~ "Northeastern Connecticut Planning Region",
state == "Connecticut" &
census_region == "New Haven County, Connecticut" ~ "South Central Connecticut Planning Region",
state == "Connecticut" &
census_region == "New London Count, Connecticut" ~ "Southeastern Connecticut Planning Region",
state == "Connecticut" &
census_region == "Fairfield County, Connecticut" ~ "Western Connecticut Planning Region",
state == "Connecticut" &
census_region == "Tolland County" ~ "Capitol Planning Region",
census_region == "Doña Ana County" ~ "Donna Ana County",
census_region == "Chugach Census Area" ~ "Valdez-Cordova Census Area",
census_region == "Copper River Census Area" ~ "Valdez-Cordova Census Area",
T ~ census_region
)) |>
dplyr::mutate(
fips = dplyr::case_when(state == "Connecticut" & county == "Northwest Hills Planning Region" ~ "09160",
state == "Connecticut" & county == "Greater Bridgeport Planning Region" ~ "09120",
state == "Connecticut" & county == "Lower Connecticut River Valley Planning Region" ~ "09130",
state == "Connecticut" & county == "Naugatuck Valley Planning Region" ~ "09140",
state == "Connecticut" & county == "Northeastern Connecticut Planning Region" ~ "09150",
state == "Connecticut" & county == "South Central Connecticut Planning Region" ~ "09170",
state == "Connecticut" & county == "Southeastern Connecticut Planning Region" ~ "09180",
state == "Connecticut" & county == "Western Connecticut Planning Region" ~ "09190",
state == "Connecticut" & county == "Capitol Planning Region" ~ "09110",
T ~ fips)
)
gg_a11_by_county <-
usmap::plot_usmap(
data = out_data,
regions = "counties",
exclude = c('AK','HI'),
#include = c('AK', 'HI'),
values = "gf",
color = "black",
linewidth = 0.1
) +
viridis::scale_fill_viridis(option = "plasma", direction = 1)
gg_a11_by_county
D - A11 by CA County
info_by_county <- CensusHLA::census_adjusted_nmdp_hla_frequencies_by_county |>
dplyr::filter(state %in% c('California')) |>
dplyr::filter(allele %in% c('A*11:01','A*02:01','A*03:01'))
out_data <- info_by_county |>
dplyr::ungroup() |>
dplyr::filter(allele == 'A*11:01') |>
dplyr::group_by(region, state, census_region, county, fips, loci, allele) |>
dplyr::summarize(gf = sum(us_2020_nmdp_gf)) |>
dplyr::filter(!(is.na(gf))) |>
# Create a STATEFP and COUNTYFP column by breaking the fips column on the 3rd character to the end
dplyr::mutate(STATEFP = substr(fips, 1, 2),
COUNTYFP = substr(fips, 3, nchar(fips)))
## `summarise()` has grouped output by 'region', 'state', 'census_region',
## 'county', 'fips', 'loci'. You can override using the `.groups` argument.
gg_a11_in_ca <-
usmap::plot_usmap(
data = out_data,
regions = "counties",
include = c('CA'),
values = "gf",
color = "black",
linewidth = 0.1
) +
viridis::scale_fill_viridis(option = "plasma", direction = 1)
gg_a11_in_ca
E A11 by CA by H4 Hexagon
## INFO [2025-04-15 14:05:07] Working with state: CA
## Adding missing grouping variables: `census_region`
## Reading layer `tl_2020_06_tract' from data source
## `/tmp/RtmpNVZfXa/temp_libpath20d2410713c6/CensusHLA/extdata/tiger_2020/tract/tl_2020_06_tract.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 9129 features and 12 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -124.482 ymin: 32.52883 xmax: -114.1312 ymax: 42.0095
## Geodetic CRS: NAD83
## INFO [2025-04-15 14:05:51] Working with state: CA
## Data has been transformed to EPSG:4326.
## Warning: st_centroid assumes attributes are constant over geometries
## Data has been transformed to EPSG:4326.
## Joining with `by = join_by(nmdp_race_code)`
F A11 Catchment
gg_catchment <- plot_delNero2022_catchment_areas(
query_allele = 'A*11:01',
CensusHLA::a11_catchment_summed$sf_tract_centroids_for_all_states_with_catchment_with_us_population_race_code_percentages_by_tract_summed
)
## Warning in layer_sf(geom = GeomSf, data = data, mapping = mapping, stat = stat,
## : Ignoring unknown parameters: `line`
A - B:58:01 by County
info_by_county <- CensusHLA::census_adjusted_nmdp_hla_frequencies_by_county |>
dplyr::filter(!(state %in% c('Alaska','Hawaii'))) |>
dplyr::filter(allele == 'B*58:01')
out_data <- info_by_county |>
dplyr::ungroup() |>
dplyr::filter(allele == 'B*58:01') |>
dplyr::group_by(region, state, census_region, county, fips, loci, allele) |>
dplyr::summarize(gf = sum(us_2020_nmdp_gf)) |>
dplyr::filter(!(is.na(gf))) |>
# Create a STATEFP and COUNTYFP column by breaking the fips column on the 3rd character to the end
dplyr::mutate(STATEFP = substr(fips, 1, 2),
COUNTYFP = substr(fips, 3, nchar(fips)))
## `summarise()` has grouped output by 'region', 'state', 'census_region',
## 'county', 'fips', 'loci'. You can override using the `.groups` argument.
out_data <-
out_data |>
dplyr::mutate(
county = dplyr::case_when(
state == "Connecticut" &
census_region == "Litchfield County, Connecticut" ~ "Northwest Hills Planning Region",
state == "Connecticut" &
census_region == "Hartford County, Connecticut" ~ "Capitol Planning Region",
state == "Connecticut" &
census_region == "Middlesex County, Connecticut" ~ "Lower Connecticut River Valley Planning Region",
state == "Connecticut" &
census_region == "Windham County, Connecticut" ~ "Northeastern Connecticut Planning Region",
state == "Connecticut" &
census_region == "New Haven County, Connecticut" ~ "South Central Connecticut Planning Region",
state == "Connecticut" &
census_region == "New London Count, Connecticut" ~ "Southeastern Connecticut Planning Region",
state == "Connecticut" &
census_region == "Fairfield County, Connecticut" ~ "Western Connecticut Planning Region",
state == "Connecticut" &
census_region == "Tolland County" ~ "Capitol Planning Region",
census_region == "Doña Ana County" ~ "Donna Ana County",
census_region == "Chugach Census Area" ~ "Valdez-Cordova Census Area",
census_region == "Copper River Census Area" ~ "Valdez-Cordova Census Area",
T ~ census_region
)) |>
dplyr::mutate(
fips = dplyr::case_when(state == "Connecticut" & county == "Northwest Hills Planning Region" ~ "09160",
state == "Connecticut" & county == "Greater Bridgeport Planning Region" ~ "09120",
state == "Connecticut" & county == "Lower Connecticut River Valley Planning Region" ~ "09130",
state == "Connecticut" & county == "Naugatuck Valley Planning Region" ~ "09140",
state == "Connecticut" & county == "Northeastern Connecticut Planning Region" ~ "09150",
state == "Connecticut" & county == "South Central Connecticut Planning Region" ~ "09170",
state == "Connecticut" & county == "Southeastern Connecticut Planning Region" ~ "09180",
state == "Connecticut" & county == "Western Connecticut Planning Region" ~ "09190",
state == "Connecticut" & county == "Capitol Planning Region" ~ "09110",
T ~ fips)
)
gg_b58_by_county <-
usmap::plot_usmap(
data = out_data,
regions = "counties",
exclude = c('AK','HI'),
#include = c('AK', 'HI'),
values = "gf",
color = "black",
linewidth = 0.1
) +
viridis::scale_fill_viridis(option = "plasma", direction = 1)
gg_b58_by_county
B - B58:01 in MS by County
info_by_county <- CensusHLA::census_adjusted_nmdp_hla_frequencies_by_county |>
dplyr::filter(state %in% c('Mississippi')) |>
dplyr::filter(allele %in% c('B*58:01'))
out_data <- info_by_county |>
dplyr::ungroup() |>
dplyr::filter(allele == 'B*58:01') |>
dplyr::group_by(region, state, census_region, county, fips, loci, allele) |>
dplyr::summarize(gf = sum(us_2020_nmdp_gf)) |>
dplyr::filter(!(is.na(gf))) |>
# Create a STATEFP and COUNTYFP column by breaking the fips column on the 3rd character to the end
dplyr::mutate(STATEFP = substr(fips, 1, 2),
COUNTYFP = substr(fips, 3, nchar(fips)))
## `summarise()` has grouped output by 'region', 'state', 'census_region',
## 'county', 'fips', 'loci'. You can override using the `.groups` argument.
gg_b58_in_ms <-
usmap::plot_usmap(
data = out_data,
regions = "counties",
include = c('MS'),
values = "gf",
color = "black",
linewidth = 0.1
) +
viridis::scale_fill_viridis(option = "plasma", direction = 1) +
theme(legend.position = 'right' )
gg_b58_in_ms
C - B58:01 in MS by Hexagon [X}]
## INFO [2025-04-15 14:06:45] Working with state: MS
## Adding missing grouping variables: `census_region`
## Reading layer `tl_2020_28_tract' from data source
## `/tmp/RtmpNVZfXa/temp_libpath20d2410713c6/CensusHLA/extdata/tiger_2020/tract/tl_2020_28_tract.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 878 features and 12 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -91.65501 ymin: 30.13984 xmax: -88.09789 ymax: 34.9961
## Geodetic CRS: NAD83
## INFO [2025-04-15 14:06:51] Working with state: MS
## Data has been transformed to EPSG:4326.
## Warning: st_centroid assumes attributes are constant over geometries
## Data has been transformed to EPSG:4326.
## Joining with `by = join_by(nmdp_race_code)`
D - B58:01 Catchment
gg_catchment <- plot_delNero2022_catchment_areas(
query_allele = 'B*58:01',
CensusHLA::b58_catchment_summed$sf_tract_centroids_for_all_states_with_catchment_with_us_population_race_code_percentages_by_tract_summed
)
## Warning in layer_sf(geom = GeomSf, data = data, mapping = mapping, stat = stat,
## : Ignoring unknown parameters: `line`
Tables
Table 1: United States 2020 Census Adjusted HLA-A*11:01 Genotypic
Frequencies
CensusHLA::us_pop_multirace_in_nmdp_codes |>
dplyr::left_join(
CensusHLA::nmdp_hla_frequencies_by_race_us_2020_census_adjusted |>
dplyr::filter(allele == 'A*11:01') |>
dplyr::select(allele, allele, nmdp_race_code,us_2020_percent_pop,nmdp_calc_gf,us_2020_nmdp_gf) |>
dplyr::arrange(desc(us_2020_percent_pop))
) |>
# Convert percentages and gfs to percentages
dplyr::mutate(
us_2020_percent_pop = us_2020_percent_pop * 100,
nmdp_calc_gf = nmdp_calc_gf * 100,
us_2020_nmdp_gf = us_2020_nmdp_gf * 100
) |>
# Round percentages and gf to 1 digit after decimal
dplyr::mutate(
us_2020_percent_pop = round(us_2020_percent_pop, 1),
nmdp_calc_gf = round(nmdp_calc_gf, 1),
us_2020_nmdp_gf = round(us_2020_nmdp_gf, 1)
) |>
dplyr::select(
`Ethnic Code` = nmdp_race_code,
`Allele` = allele,
`Single Race Population` = total_single_race_pop,
`Multi-Race Population ` = total_multiple_race_pop,
`Total Population` = total_2020_pop,
`Percentage of Total Pop` = us_2020_percent_pop,
`NMDP Calcualted GF` = nmdp_calc_gf,
`Population-Adjusted GF` = us_2020_nmdp_gf
)
## Joining with `by = join_by(nmdp_race_code)`
## # A tibble: 6 × 8
## `Ethnic Code` Allele `Single Race Population` `Multi-Race Population `
## <chr> <chr> <dbl> <dbl>
## 1 AFA A*11:01 39940338 2064019
## 2 API A*11:01 20240737 1820295
## 3 CAU A*11:01 191697647 5944911
## 4 HIS A*11:01 62080044 0
## 5 NAM A*11:01 2251699 2131361
## 6 UNK NA 1689833 1419206
## # ℹ 4 more variables: `Total Population` <dbl>,
## # `Percentage of Total Pop` <dbl>, `NMDP Calcualted GF` <dbl>,
## # `Population-Adjusted GF` <dbl>
Table 2: HLA-A*11:01 Population-adjusted genotypic frequencies for
top 11 NCI Catchment areas.
CensusHLA::a11_catchment_summed$sf_tract_centroids_for_all_states_with_catchment_with_us_population_race_code_percentages_by_tract_summed |> dplyr::select(-geometry) |> dplyr::mutate(patient_pop = total_2020_pop * us_2020_nmdp_gf_sum) |> dplyr::arrange(desc(patient_pop)) |> DT::datatable(
,filter = 'top'
,rownames = FALSE
,extensions = 'Buttons', options = list(
scrollX=TRUE,
pageLength = 11,
dom = 'Bfrtip',
buttons = c('copy', 'csv', 'excel', 'pdf', 'colvis')
)
)
## Warning in instance$preRenderHook(instance): It seems your data is too big for
## client-side DataTables. You may consider server-side processing:
## https://rstudio.github.io/DT/server.html
Supplemental 1 - California County population-adjusted HLA-A*11:01
Genotypic frequencies
info_by_county <- CensusHLA::census_adjusted_nmdp_hla_frequencies_by_county |>
dplyr::filter(state %in% c('California')) |>
dplyr::filter(allele %in% c('A*11:01','A*02:01','A*03:01'))
out_data <- info_by_county |>
dplyr::ungroup() |>
dplyr::filter(allele == 'A*11:01') |>
dplyr::group_by(region, state, census_region, county, fips, loci, allele) |>
dplyr::summarize(gf = sum(us_2020_nmdp_gf),
population = sum(total_2020_pop)) |>
dplyr::filter(!(is.na(gf))) |>
# Create a STATEFP and COUNTYFP column by breaking the fips column on the 3rd character to the end
dplyr::mutate(STATEFP = substr(fips, 1, 2),
COUNTYFP = substr(fips, 3, nchar(fips)))
## `summarise()` has grouped output by 'region', 'state', 'census_region',
## 'county', 'fips', 'loci'. You can override using the `.groups` argument.
out_data <- out_data |>
dplyr::arrange(desc(gf))
out_data
## # A tibble: 58 × 11
## # Groups: region, state, census_region, county, fips, loci [58]
## region state census_region county fips loci allele gf population STATEFP
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <chr>
## 1 us Cali… Santa Clara … Santa… 06085 A A*11:… 0.189 1918185 06
## 2 us Cali… San Francisc… San F… 06075 A A*11:… 0.178 862909 06
## 3 us Cali… San Mateo Co… San M… 06081 A A*11:… 0.172 753795 06
## 4 us Cali… Alameda Coun… Alame… 06001 A A*11:… 0.170 1662962 06
## 5 us Cali… Orange Count… Orang… 06059 A A*11:… 0.152 3159127 06
## 6 us Cali… Sutter Count… Sutte… 06101 A A*11:… 0.143 98545 06
## 7 us Cali… Contra Costa… Contr… 06013 A A*11:… 0.141 1150646 06
## 8 us Cali… Trinity Coun… Trini… 06105 A A*11:… 0.140 15896 06
## 9 us Cali… Sacramento C… Sacra… 06067 A A*11:… 0.140 1564896 06
## 10 us Cali… San Joaquin … San J… 06077 A A*11:… 0.136 771652 06
## # ℹ 48 more rows
## # ℹ 1 more variable: COUNTYFP <chr>
Supplemental 2 - United States 2020 Census Adjusted HLA-B*58:01
Genotypic Frequencies for Mississippi
CensusHLA::us_pop_multirace_in_nmdp_codes |>
dplyr::left_join(
CensusHLA::census_adjusted_nmdp_hla_frequencies_by_state |> dplyr::filter(allele == 'B*58:01') |>
dplyr::filter(census_region == 'Mississippi') |>
dplyr::select(allele,census_region,nmdp_race_code,us_2020_percent_pop,nmdp_calc_gf,us_2020_nmdp_gf) |>
dplyr::arrange(desc(us_2020_percent_pop))
) |>
# Convert percentages and gfs to percentages
dplyr::mutate(
us_2020_percent_pop = us_2020_percent_pop * 100,
nmdp_calc_gf = nmdp_calc_gf * 100,
us_2020_nmdp_gf = us_2020_nmdp_gf * 100
) |>
# Round percentages and gf to 1 digit after decimal
dplyr::mutate(
us_2020_percent_pop = round(us_2020_percent_pop, 1),
nmdp_calc_gf = round(nmdp_calc_gf, 1),
us_2020_nmdp_gf = round(us_2020_nmdp_gf, 1)
) |>
dplyr::select(
`Region` = census_region,
`Ethnic Code` = nmdp_race_code,
`Allele` = allele,
`Single Race Population` = total_single_race_pop,
`Multi-Race Population ` = total_multiple_race_pop,
`Total Population` = total_2020_pop,
`Percentage of Total Pop` = us_2020_percent_pop,
`NMDP Calcualted GF` = nmdp_calc_gf,
`Population-Adjusted GF` = us_2020_nmdp_gf
)
## Joining with `by = join_by(nmdp_race_code)`
## # A tibble: 6 × 9
## Region `Ethnic Code` Allele Single Race Populati…¹ Multi-Race Populatio…²
## <chr> <chr> <chr> <dbl> <dbl>
## 1 Mississippi AFA B*58:… 39940338 2064019
## 2 Mississippi API B*58:… 20240737 1820295
## 3 Mississippi CAU B*58:… 191697647 5944911
## 4 Mississippi HIS B*58:… 62080044 0
## 5 Mississippi NAM B*58:… 2251699 2131361
## 6 NA UNK NA 1689833 1419206
## # ℹ abbreviated names: ¹`Single Race Population`, ²`Multi-Race Population `
## # ℹ 4 more variables: `Total Population` <dbl>,
## # `Percentage of Total Pop` <dbl>, `NMDP Calcualted GF` <dbl>,
## # `Population-Adjusted GF` <dbl>
#dplyr::select(allele, us_2020_nmdp_gf) |>
#dplyr::summarize(gf = sum(us_2020_nmdp_gf))
Supplemental 3 - Mississippi County population-adjusted HLA-B*58:01
Genotypic frequencies
info_by_county <- CensusHLA::census_adjusted_nmdp_hla_frequencies_by_county |>
dplyr::filter(state %in% c('Mississippi')) |>
dplyr::filter(allele %in% c('B*58:01'))
out_data <- info_by_county |>
dplyr::ungroup() |>
dplyr::filter(allele == 'B*58:01') |>
dplyr::group_by(region, state, census_region, county, fips, loci, allele) |>
dplyr::summarize(gf = sum(us_2020_nmdp_gf),
population = sum(total_2020_pop)) |>
dplyr::filter(!(is.na(gf))) |>
# Create a STATEFP and COUNTYFP column by breaking the fips column on the 3rd character to the end
dplyr::mutate(STATEFP = substr(fips, 1, 2),
COUNTYFP = substr(fips, 3, nchar(fips)))
## `summarise()` has grouped output by 'region', 'state', 'census_region',
## 'county', 'fips', 'loci'. You can override using the `.groups` argument.
out_data <- out_data |>
dplyr::arrange(desc(gf))
out_data
## # A tibble: 82 × 11
## # Groups: region, state, census_region, county, fips, loci [82]
## region state census_region county fips loci allele gf population
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 us Mississippi Claiborne Cou… Claib… 28021 B B*58:… 0.0679 9112
## 2 us Mississippi Jefferson Cou… Jeffe… 28063 B B*58:… 0.0667 7238
## 3 us Mississippi Holmes County… Holme… 28051 B B*58:… 0.0659 16964
## 4 us Mississippi Humphreys Cou… Humph… 28053 B B*58:… 0.0629 7762
## 5 us Mississippi Tunica County… Tunic… 28143 B B*58:… 0.0622 9715
## 6 us Mississippi Coahoma Count… Coaho… 28027 B B*58:… 0.0619 21314
## 7 us Mississippi Leflore Count… Leflo… 28083 B B*58:… 0.0608 28286
## 8 us Mississippi Quitman Count… Quitm… 28119 B B*58:… 0.0602 6159
## 9 us Mississippi Washington Co… Washi… 28151 B B*58:… 0.0592 44791
## 10 us Mississippi Sharkey Count… Shark… 28125 B B*58:… 0.0587 3778
## # ℹ 72 more rows
## # ℹ 2 more variables: STATEFP <chr>, COUNTYFP <chr>
Supplemental 4 - HLA-B*58:01 Population-adjusted genotypic
frequencies by NCI Catchment areas.
CensusHLA::b58_catchment_summed$sf_tract_centroids_for_all_states_with_catchment_with_us_population_race_code_percentages_by_tract_summed |> dplyr::select(-geometry) |> dplyr::mutate(patient_pop = total_2020_pop * us_2020_nmdp_gf_sum) |> dplyr::arrange(desc(patient_pop)) |> DT::datatable(
,filter = 'top'
,rownames = FALSE
,extensions = 'Buttons', options = list(
scrollX=TRUE,
pageLength = 11,
dom = 'Bfrtip',
buttons = c('copy', 'csv', 'excel', 'pdf', 'colvis')
)
)
## Warning in instance$preRenderHook(instance): It seems your data is too big for
## client-side DataTables. You may consider server-side processing:
## https://rstudio.github.io/DT/server.html
System and Session info
## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Rocky Linux 9.4 (Blue Onyx)
##
## Matrix products: default
## BLAS/LAPACK: FlexiBLAS OPENBLAS-OPENMP; LAPACK version 3.9.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] dplyr_1.1.4 ggplot2_3.5.1 CensusHLA_0.1.0.9000
##
## loaded via a namespace (and not attached):
## [1] gtable_0.3.6 xfun_0.49
## [3] bslib_0.8.0 htmlwidgets_1.6.4
## [5] tigris_2.1 crosstalk_1.2.1
## [7] vctrs_0.6.5 tools_4.4.1
## [9] generics_0.1.3 curl_6.0.1
## [11] tibble_3.2.1 proxy_0.4-27
## [13] pkgconfig_2.0.3 KernSmooth_2.23-26
## [15] desc_1.4.3 uuid_1.2-1
## [17] lifecycle_1.0.4 h3jsr_1.3.1
## [19] compiler_4.4.1 farver_2.1.2
## [21] stringr_1.5.1 textshaping_0.4.1
## [23] munsell_0.5.1 terra_1.8-5
## [25] codetools_0.2-20 htmltools_0.5.8.1
## [27] class_7.3-23 sass_0.4.9
## [29] yaml_2.3.10 tidyr_1.3.1
## [31] pillar_1.10.0 pkgdown_2.1.1
## [33] jquerylib_0.1.4 DT_0.33
## [35] classInt_0.4-10 cachem_1.1.0
## [37] wk_0.9.4 viridis_0.6.5
## [39] tidyselect_1.2.1 digest_0.6.37
## [41] censusapi_0.8.0 stringi_1.8.4
## [43] purrr_1.0.4 sf_1.0-19
## [45] labeling_0.4.3 rnaturalearth_1.0.1
## [47] fastmap_1.2.0 grid_4.4.1
## [49] colorspace_2.1-1 cli_3.6.4
## [51] magrittr_2.0.3 utf8_1.2.4
## [53] e1071_1.7-16 withr_3.0.2
## [55] scales_1.3.0 rappdirs_0.3.3
## [57] rmarkdown_2.29 lambda.r_1.2.4
## [59] httr_1.4.7 gridExtra_2.3
## [61] futile.logger_1.4.3 rnaturalearthhires_1.0.0.9000
## [63] ragg_1.3.3 evaluate_1.0.1
## [65] knitr_1.49 V8_6.0.0
## [67] viridisLite_0.4.2 s2_1.1.7
## [69] rlang_1.1.5 futile.options_1.0.1
## [71] usmap_0.7.1 Rcpp_1.0.13-1
## [73] glue_1.8.0 DBI_1.2.3
## [75] geojsonsf_2.0.3 formatR_1.14
## [77] rstudioapi_0.17.1 usmapdata_0.3.0
## [79] jsonlite_1.8.9 R6_2.5.1
## [81] systemfonts_1.1.0 fs_1.6.5
## [83] units_0.8-5
## sysname
## "Linux"
## release
## "5.14.0-427.22.1.el9_4.x86_64"
## version
## "#1 SMP PREEMPT_DYNAMIC Wed Jun 19 17:35:04 UTC 2024"
## nodename
## "ip-10-110-10-102.us-west-2.compute.internal"
## machine
## "x86_64"
## login
## "unknown"
## user
## "christian.roy"
## effective_user
## "christian.roy"